Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

By Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yinhai Wang, Yunpeng Wang. Italian National Conference on Sensors, 2017

1,306 citations recorded by the source corpus.

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Subfields: Building and Construction, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/4dd13420941869ce532360fca14669f551581f0f

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Citations in the corpus

A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “Caltrans PEMS Point [5], [50], [51], [52] 14 out of 37 Beijing dataset Point [45], [46], [47] 6 out of 37 Beijing dataset Trajectory [4], [53], [54] 6 out of 37”
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting (2020, methodology): “[15] [16] adopt CNNs to extract the spatial features in which trafﬁc networks are converted to regular grids.”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “There are two important factors that need to be considered when constructing a CNN network: (a) the hyper-parameters concerned with the convolutional and pooling layers, such as the convolutional filter size, pooling size, and pooling method; and (b) the depth of the CNN (Ma et al. 2017).”
Process (2020, background): “Most of the previous works [10, 11, 12, 13, 14, 15, 16, 17, 18, 19] on traffic modeling focus on predicting speed, volume, density and travel time, which have achieved remarkable success in this field.”
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017, background): “Wu & Tan (2016) and Ma et al. (2017) model the spatial correlation with Convolutional Neural Networks (CNN), but the spatial structure is in the Euclidean space (e.g., 2D images).”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, background): “Application domains Related works Transportation [189], [199], [125], [32], [30], [6], [33], [136], [60], [121], [27], [114], [177], [90], [23], [73], [117], [135], [89], [148], [85], [137], [155], [157], [79], [107], [201], [22], [24], [108], [105], [16], [106], [93], [15], [175], [176], [104], [14”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, background): “[71] present an approach to traffic flow prediction”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, background): “There are many studies forecasting traﬃc from other perspectives, like learning traﬃc as images Ma et al. (2017) or graphs Cui et al. (2019) by combining convolutional based neural networks.”
LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (2020, neutral): “…to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years such as convolutional neural network (CNN) and recurrent neural networks(RNN) [Wu and Tan, 2016; Ma et al., 2017; Hochreiter and Schmidhuber, 1997; Xingjian et al., 2015].”
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting (2020, background): “…Transformer is only able to handle the sequential dependency between the source and target sequences in machine translation, while spatial (network information) and temporal (sequential information) dependencies in traffic data are prominent (Cui, Henrickson, Ke, & Wang, 2019; Ma et al., 2017).”